Police forces, hospitals and councils struggle to understand how to use artificial intelligence because of a lack of clear ethical guidance from the government, according to the country's only surveillance regulator. The surveillance camera commissioner, Tony Porter, said he received requests for guidance all the time from public bodies which do not know where the limits lie when it comes to the use of facial, biometric and lip-reading technology. "Facial recognition technology is now being sold as standard in CCTV systems, for example, so hospitals are having to work out if they should use it," Porter said. "Police are increasingly wearing body cameras. What are the appropriate limits for their use? "The problem is that there is insufficient guidance for public bodies to know what is appropriate and what is not, and the public have no idea what is going on because there is no real transparency." The watchdog's comments came as it emerged that Downing Street had commissioned a review led by the Committee on Standards in Public Life, whose chairman had called on public bodies to reveal when they use algorithms in decision making. Lord Evans, a former MI5 chief, told the Sunday Telegraph that "it was very difficult to find out where AI is being used in the public sector" and that "at the very minimum, it should be visible, and declared, where it has the potential for impacting on civil liberties and human rights and freedoms". AI is increasingly deployed across the public sector in surveillance and elsewhere. The high court ruled in September that the police use of automatic facial recognition technology to scan people in crowds was lawful. Its use by South Wales police was challenged by Ed Bridges, a former Lib Dem councillor, who noticed the cameras when he went out to buy a lunchtime sandwich, but the court held that the intrusion into privacy was proportionate. Durham police have spent three years evaluating an AI tool devised by Cambridge University to predict whether an arrested person is likely to reoffend and so should not be released on bail. Similar technologies used in the US, where they are also guide sentencing, have been accused of concluding that black people are more likely to be future criminals, but the results of the British trial are yet to be made public. The committee is due to report to Boris Johnson in February, but Porter said the task was urgent because of the rapid pace of technological change and an unclear system of regulation in which no single body had oversight. The information commissioner is responsible for the use of personal data but not surveillance, while Porter's office regulates the use of CCTV systems and all technologies attached to them, including facial recognition and lip-reading software. "We've been calling for a wider review for months," Porter said. "The SCC, for example, is the only surveillance regulator in England and Wales and we date back to when the iPhone 5 was new and exciting.

In 2018 we witnessed a clash of titans as government and tech companies collided on privacy issues around collecting, culling and using personal data. From GDPR to Facebook scandals, many tech CEOs were defending big data, its use, and how they're safeguarding the public. Meanwhile, the public was amazed at technological advances like Boston Dynamic's Atlas robot doing parkour, while simultaneously being outraged at the thought of our data no longer being ours and Alexa listening in on all our conversations. For better or worse, advanced technologies like artificial intelligence has captured the public's, policymakers', and big business' imaginations. I see four AI use and ethics trends set to disrupt classrooms and conference rooms.

This post is part of Faveeo's, 20 Ideas For 2020, project. The features and techniques of Cybersecurity are continuously improving and becoming more complex as white-hat hackers try to keep up with black-hat hackers and vice versa. The power of the internet is changing the Cybersecurity landscape and we will start to see the effects of that rapid acceleration this decade. Here are some guiding questions and predictions to consider. Organizations will use AI more than ever before because of the increased availability of AI based solutions, improved accuracy, and efficiency.

The topic du jour for tech regulation is not as you might expect, data, but rather a sexy new topic of interest to policymakers – artificial intelligence (AI). It has all the glamour of Hollywood movies, all the fear that propels despots to power, and it comes complete with simple sentences and graphics that make it a Trumpian communicator's dream. We get tired of hearing it, but it's so true: technology is changing rapidly. The speed of change continues to accelerate and, let's face it, regulators and policymakers do a poor job of understanding technology, much less creating effective regulation for it. In the chaos however, there are repetitive patterns.

Artificial Intelligence is seen by many as a great transformative tech. These questions make people shift from thinking purely about the functional capabilities to the ethics behind creating such powerful and potentially life-consequential technologies. As such, it makes sense to spend time considering what we want these systems to do and make sure we address ethical questions now so that we build these systems with the common good of humanity in mind. Will AI replace human workers? The most immediate concern for many is that AI-enabled systems will replace workers across a wide range of industries.

Adaptive signal control system (ASCS) is the most advanced t raffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time in order to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adj ust the signal phasing and timings for different traffic movements at a signalized intersection with A SCS. The accuracy of the queue length prediction model varies based on the many factors, such as th e stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and satur ated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to devel op short-term queue length prediction models for signalized intersections that can be leveraged by adapt ive traffic signal control systems using four variations of Grey systems: (i) the first order single variab le Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst mo del (GVM), and (iv) GVM with Fourier error corrections (EGVM). The efficacy of the Grey models is th at they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike stat istical models. We have conducted a case study using queue length data from five intersections with ad aptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models w ere compared with linear, nonlinear time series models, and long short-term memory (LSTM) neura l network. Based on our analyses, we found that EGVM reduces the prediction error over closest co mpeting models (i.e., LSTM and Additive Autoregressive (AAR) time series models) in predicting ave rage and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error (R MSE), and 51% and 50%, respectively, in terms of Mean Absolute Error (MAE).

There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.

UK School of Engineering & Applied Science, Aston University, Birmingham B4 7ET, UK Abstract In a diffusion process on a network, how many nodes are expected to be influenced by a set of initial spreaders? This natural problem, often referred to as influence estimation, boils down to computing the marginal probability that a given node is active at a given time when the process starts from specified initial condition. Among many other applications, this task is crucial for a well-studied problem of influence maximization: finding optimal spreaders in a social network that maximize the influence spread by a certain time horizon. Indeed, influence estimation needs to be called multiple times for comparing candidate seed sets. Unfortunately, in many models of interest an exact computation of marginals is #P-hard. In practice, influence is often estimated using Monte-Carlo sampling methods that require a large number of runs for obtaining a high-fidelity prediction, especially at large times. It is thus desirable to develop analytic techniques as an alternative to sampling methods. Here, we suggest an algorithm for estimating the influence function in popular independent cascade model based on a scalable dynamic message-passing approach. This method has a computational complexity of a single Monte-Carlo simulation and provides an upper bound on the expected spread on a general graph, yielding exact answer for treelike networks. We also provide dynamic message-passing equations for a stochastic version of the linear threshold model. The resulting saving of a potentially large sampling factor in the running time compared to simulation-based techniques hence makes it possible to address large-scale problem instances.

One of the most popular movie franchises of our time is the "Terminator" series, launched back in the early 1980s and featuring six-time Mr. America bodybuilder Arnold Schwarzenegger as a futuristic humanoid killing machine As noted by Great Power War, the backstory to the film is that the creation of the nearly-invincible cyborg Terminators stemmed from a "SkyNet" computer system that controlled U.S. nuclear weapons and "got smart," eventually seeing all humans as its enemy. So, in one fell swoop, the system launched its missiles at pre-programmed targets, which, of course, invited a second-strike counter-launch and created a nuclear holocaust that nearly destroyed all of humankind. While the Terminator series never really identified the'smart' SkyNet computer system as having artificial intelligence, some years later after AI became more of a thing it was understood that's the kind of system the fictional SkyNet operated. The "machine-learning" aspect of AI is how SkyNet "got smart" one day and launched the nuclear payloads it controlled. But the Terminator series are just movies, right?

Many governments worldwide have begun to see the deployment of artificial intelligence as strategic importance for their country. Whereas in decades past, only a few developed nations spent any of their budgets on AI research and advancement, now it seems almost every country has invested in it. However, these countries differ on their basic approaches to privacy, data transparency and the connection between the economy and governmental oversight. Western countries operate on varying levels of government oversight over business operations, while China has a closer cooperation between government and business activities while being slow to regulate privacy and data transparency. The problem with regulating AI is that it is not a discrete technology but a collection of different technologies and patterns that use machine learning to achieve different objectives.